Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancers experienced by patients, leading to large numbers of cancer related deaths around the world. While the stage of NSCLC is frequently used to determine the type of treatment administered to an NSCLC patient, surgical resection of tumor tissue still remains a leading option for early stage NSCLC. Although some patients who undergo surgical resection of tumor tissue will be cured, a significant subset of patients who under surgical resection will develop a recurrence of NSCLC and subsequently die from the disease. Adjuvant treatment with platinum based doublet chemotherapy (adjuvant chemotherapy) is the standard of care for NSCLC patients following surgical resection. However, a majority of these patients will not receive any additional benefit from the adjuvant chemotherapy, and will instead be subjected to unnecessary suffering and other deleterious effects. Additionally, a substantial portion of the over $12 billion spent annually in the United States on lung cancer care is directed to unnecessary treatment.
There are no prospectively validated and clinically relevant conventional tools to predict the additional benefit of post-surgery adjuvant chemo for early stage NSCLC. Conventional tests for NSCLC are mainly prognostic, and there are no established molecular diagnostic assays for NSCLC that have been validated in terms of their predictive ability. Furthermore, conventional prognostic approaches are often not reimbursed and thus have poor market penetration. Conventional predictive molecular tests are also expensive, involve tissue destruction, and require specialized facilities. Conventional predictive approaches also have turnaround times ranging from ten to fourteen days up to several weeks, which are not clinically optimal time frames. Additionally, conventional molecular tests rely on small regions of tissue for profiling genes and proteins, and therefore do not comprehensively characterize the tumor being examined.
Conventional predictive approaches to separating NSCLC patients into low-risk or high-risk categories rely on the visual evaluation by a human pathologist of hematoxylin and eosin (H&E) stained images of lung biopsy specimens. A pathologist may manually identify, count, and grade tumor infiltrating lymphocytes (TIL) in a tumor. High densities of TILs are associated with survival for certain cancers. However, since TILs are identified, counted, and graded manually, conventional approaches to assessing risk in NSCLC patients are subjective, error prone, and suffer from inter-rater reliability issues. Since human pathologists may be challenged to reliably assess the risk of NSCLC recurrence using conventional approaches in clinically relevant time frames, ineffective therapies and procedures may be performed that ultimately result in poor outcomes for the patient. Thus, it would be beneficial if a faster, less costly, more reliable, automated approach to predicting the recurrence of NSCLC in patients were available.